U.S. patent number 11,456,834 [Application Number 17/019,137] was granted by the patent office on 2022-09-27 for adaptive demodulation reference signal (dmrs).
This patent grant is currently assigned to QUALCOMM Incorporated. The grantee listed for this patent is QUALCOMM Incorporated. Invention is credited to Sony Akkarakaran, Tao Luo, Hamed Pezeshki.
United States Patent |
11,456,834 |
Pezeshki , et al. |
September 27, 2022 |
Adaptive demodulation reference signal (DMRS)
Abstract
A method of wireless communication by a user equipment (UE)
indicates, to a base station, a training state of a machine
learning model for a given channel condition, and a request for a
change in demodulation reference signal (DMRS) transmissions. The
UE also receives DMRS transmissions in accordance with the training
state for the given channel condition. The UE performs online
training of the machine learning model with the DMRS transmissions.
A UE may also request, from a base station, a specific number of
demodulation reference signal (DMRS) symbols for a slot, and
receive DMRS transmissions in response to the request to estimate a
raw channel.
Inventors: |
Pezeshki; Hamed (San Diego,
CA), Luo; Tao (San Diego, CA), Akkarakaran; Sony
(Poway, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
QUALCOMM Incorporated |
San Diego |
CA |
US |
|
|
Assignee: |
QUALCOMM Incorporated (San
Diego, CA)
|
Family
ID: |
1000006586580 |
Appl.
No.: |
17/019,137 |
Filed: |
September 11, 2020 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20220085935 A1 |
Mar 17, 2022 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04W
72/0413 (20130101); G06N 20/00 (20190101); H04L
5/0048 (20130101) |
Current International
Class: |
H04W
72/04 (20090101); H04L 5/00 (20060101); G06N
20/00 (20190101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Honkala et al, DeepRx: Fully Convolutional Deep Learning Receiver,
arXiv, 30 pages, May 4, 2020. cited by examiner.
|
Primary Examiner: Duong; Frank
Attorney, Agent or Firm: Seyfarth Shaw LLP
Claims
What is claimed is:
1. A method of wireless communication by a user equipment (UE),
comprising: indicating, to a base station, a training state of a
machine learning model for a given channel condition, and a request
for a change in demodulation reference signal (DMRS) transmissions;
receiving DMRS transmissions in accordance with the training state
for the given channel condition; and performing online training of
the machine learning model with the DMRS transmissions.
2. The method of claim 1, in which the training state indicates a
decoding quality is above a threshold value, and the receiving
comprises receiving a light DMRS pattern.
3. The method of claim 1, in which the training state indicates a
decoding quality is above a threshold value, and the receiving
comprises receiving fewer DMRS symbols in a slot.
4. The method of claim 1, in which the training state indicates a
decoding quality is below a threshold value, and the receiving
comprises receiving a heavy DMRS pattern.
5. The method of claim 1, in which the training state indicates a
decoding quality is below a threshold value, and the receiving
comprises receiving additional DMRS symbols in a slot.
6. The method of claim 1, in which the training state is based on a
bit error rate associated with decoding by the machine learning
model.
7. The method of claim 1, in which the indicating occurs via a
physical uplink control channel.
8. The method of claim 1, further comprising indicating, to the
base station, online training as a reason for indicating the
training state.
9. The method of claim 1, in which a pattern of the received DMRS
differs from a legacy DMRS pattern.
10. A method of wireless communication by a user equipment (UE),
comprising: requesting, from a base station, a specific number of
demodulation reference signal (DMRS) symbols for a slot; and
receiving DMRS transmissions across a plurality of beams, in
accordance with the requesting to estimate a raw, non-beamformed
channel including the plurality of beams.
11. The method of claim 10, in which requesting comprises
requesting a specific number of repeated DMRS symbols.
12. The method of claim 10, in which requesting comprises
requesting a specific number of newly defined DMRS symbols.
13. The method of claim 10, in which the specific number of DMRS
symbols comprises three symbols or four symbols.
14. The method of claim 13, in which the three symbols or four
symbols comprise three consecutive symbols or four consecutive
symbols.
15. The method of claim 10, further comprising performing a beam
sweep across a specific number of beams corresponding to the
specific number of DMRS symbols to obtain an estimate of the raw,
non-beamformed channel.
16. The method of claim 10, further comprising requesting a rank
corresponding to the specific number of DMRS symbols.
17. An apparatus for wireless communication by a user equipment
(UE), comprising: a processor, memory coupled with the processor;
and instructions stored in the memory and operable, when executed
by the processor, to cause the apparatus: to indicate, to a base
station, a training state of a machine learning model for a given
channel condition, and a request for a change in demodulation
reference signal (DMRS) transmissions; to receive DMRS
transmissions in accordance with the training state for the given
channel condition; and to perform online training of the machine
learning model with the DMRS transmissions.
18. The apparatus of claim 17, in which the training state
indicates a decoding quality is above a threshold value, and in
which the processor causes the apparatus to receive a light DMRS
pattern.
19. The apparatus of claim 17, in which the training state
indicates a decoding quality is above a threshold value, and in
which the processor causes the apparatus to receive fewer DMRS
symbols in a slot.
20. The apparatus of claim 17, in which the training state
indicates a decoding quality is below a threshold value, and in
which the processor causes the apparatus to receive a heavy DMRS
pattern.
21. The apparatus of claim 17, in which the training state
indicates a decoding quality is below a threshold value, and in
which the processor causes the apparatus to receive additional DMRS
symbols in a slot.
22. The apparatus of claim 17, in which the training state is based
on a bit error rate associated with decoding by the machine
learning model.
23. The apparatus of claim 17, in which the processor causes the
apparatus to indicate via a physical uplink control channel.
24. The apparatus of claim 17, in which the processor causes the
apparatus to indicate, to the base station, online training as a
reason for indicating the training state.
25. The apparatus of claim 17, in which a pattern of the received
DMRS differs from a legacy DMRS pattern.
26. An apparatus for wireless communication by a user equipment
(UE), comprising: a processor, memory coupled with the processor;
and instructions stored in the memory and operable, when executed
by the processor, to cause the apparatus: to request, from a base
station, a specific number of demodulation reference signal (DMRS)
symbols for a slot; and to receive DMRS transmissions across a
plurality of beams, in accordance with the requesting to estimate a
raw, non-beamformed channel including the plurality of beams.
27. The apparatus of claim 26, in which the processor causes the
apparatus to request a specific number of repeated DMRS
symbols.
28. The apparatus of claim 26, in which the processor causes the
apparatus to request a specific number of newly defined DMRS
symbols.
29. The apparatus of claim 26, in which the specific number of DMRS
symbols comprises three symbols or four symbols.
30. The apparatus of claim 29, in which the three symbols or four
symbols comprise three consecutive symbols or four consecutive
symbols.
31. The apparatus of claim 26, in which the processor causes the
apparatus to perform a beam sweep across a specific number of beams
corresponding to the specific number of DMRS symbols to obtain an
estimate of the raw, non-beamformed channel.
32. The apparatus of claim 26, in which the processor causes the
apparatus to request a rank corresponding to the specific number of
DMRS symbols.
33. A user equipment (UE) for wireless communications, comprising:
means for indicating, to a base station, a training state of a
machine learning model for a given channel condition, and a request
for a change in demodulation reference signal (DMRS) transmissions;
means for receiving DMRS transmissions in accordance with the
training state for the given channel condition; and means for
performing online training of the machine learning model with the
DMRS transmissions.
34. The UE of claim 33, in which the training state indicates a
decoding quality is above a threshold value, and the receiving
means comprises means for receiving a light DMRS pattern.
35. The UE of claim 33, in which the training state indicates a
decoding quality is above a threshold value, and the receiving
means comprises means for receiving fewer DMRS symbols in a
slot.
36. The UE of claim 33, in which the training state indicates a
decoding quality is below a threshold value, and the receiving
means comprises means for receiving a heavy DMRS pattern.
37. The UE of claim 33, in which the training state indicates a
decoding quality is below a threshold value, and the receiving
means comprises means for receiving additional DMRS symbols in a
slot.
38. The UE of claim 33, in which the training state is based on a
bit error rate associated with decoding by the machine learning
model.
39. The UE of claim 33, in which the means for indicating operates
via a physical uplink control channel.
40. The UE of claim 33, further comprising means for indicating, to
the base station, online training as a reason for indicating the
training state.
41. The UE of claim 33, in which a pattern of the received DMRS
differs from a legacy DMRS pattern.
42. A user equipment (UE) for wireless communications, comprising:
means for requesting, from a base station, a specific number of
demodulation reference signal (DMRS) symbols for a slot; and means
for receiving DMRS transmissions across a plurality of beams, in
accordance with the requesting to estimate a raw, non-beamformed
channel including the plurality of beams.
43. The UE of claim 42, in which the requesting means comprises
means for requesting a specific number of repeated DMRS
symbols.
44. The UE of claim 42, in which the requesting means comprises
means for requesting a specific number of newly defined DMRS
symbols.
45. The UE of claim 42, in which the specific number of DMRS
symbols comprises three symbols or four symbols.
46. The UE of claim 45, in which the three symbols or four symbols
comprise three consecutive symbols or four consecutive symbols.
47. The UE of claim 42, further comprising means for performing a
beam sweep across a specific number of beams corresponding to the
specific number of DMRS symbols to obtain an estimate of the raw,
non-beamformed channel.
48. The UE of claim 42, further comprising means for requesting a
rank corresponding to the specific number of DMRS symbols.
49. A non-transitory computer-readable medium having program code
recorded thereon, the program code executed by a user equipment
(UE) and comprising: program code to indicate, to a base station, a
training state of a machine learning model for a given channel
condition, and a request for a change in demodulation reference
signal (DMRS) transmissions; program code to receive DMRS
transmissions in accordance with the training state for the given
channel condition; and program code to perform online training of
the machine learning model with the DMRS transmissions.
50. A non-transitory computer-readable medium having program code
recorded thereon, the program code executed by a user equipment
(UE) and comprising: program code to request, from a base station,
a specific number of demodulation reference signal (DMRS) symbols
for a slot; and program code to receive DMRS transmissions across a
plurality of beams, in accordance with the requesting to estimate a
raw, non-beamformed channel including the plurality of beams.
Description
FIELD OF THE DISCLOSURE
Aspects of the present disclosure generally relate to wireless
communications, and more particularly to techniques and apparatuses
for adaptive demodulation reference signal (DMRS) transmission, for
example, with machine learning-based receivers.
BACKGROUND
Wireless communications systems are widely deployed to provide
various telecommunications services such as telephony, video, data,
messaging, and broadcasts. Typical wireless communications systems
may employ multiple-access technologies capable of supporting
communications with multiple users by sharing available system
resources (e.g., bandwidth, transmit power, and/or the like).
Examples of such multiple-access technologies include code division
multiple access (CDMA) systems, time division multiple access
(TDMA) systems, frequency-division multiple access (FDMA) systems,
orthogonal frequency-division multiple access (OFDMA) systems,
single-carrier frequency-division multiple access (SC-FDMA)
systems, time division synchronous code division multiple access
(TD-SCDMA) systems, and long term evolution (LTE). LTE/LTE-Advanced
is a set of enhancements to the universal mobile telecommunications
system (UMTS) mobile standard promulgated by the Third Generation
Partnership Project (3GPP).
A wireless communications network may include a number of base
stations (BSs) that can support communications for a number of user
equipment (UEs). A user equipment (UE) may communicate with a base
station (BS) via the downlink and uplink. The downlink (or forward
link) refers to the communications link from the BS to the UE, and
the uplink (or reverse link) refers to the communications link from
the UE to the BS. As will be described in more detail, a BS may be
referred to as a Node B, a gNB, an access point (AP), a radio head,
a transmit and receive point (TRP), a new radio (NR) BS, a 5G Node
B, and/or the like.
Artificial neural networks may comprise interconnected groups of
artificial neurons (e.g., neuron models). The artificial neural
network may be a computational device or represented as a method to
be performed by a computational device. Convolutional neural
networks, such as deep convolutional neural networks, are a type of
feed-forward artificial neural network. Convolutional neural
networks may include layers of neurons that may be configured in a
tiled receptive field. It would be desirable to apply neural
network processing to wireless communications to achieve greater
efficiencies.
SUMMARY
According to an aspect of the present disclosure, a method of
wireless communication by a user equipment (UE) indicates, to a
base station, a training state of a machine learning model for a
given channel condition. The UE also requests a change in
demodulation reference signal (DMRS) transmissions. The UE receives
DMRS transmissions in response to the request, based on the
indicated training state. The UE performs online training of the
machine learning model with the received DMRS transmissions.
In another aspect, a method of wireless communication by a UE
requests, from a base station, a specific number of demodulation
reference signal (DMRS) symbols for a slot. The method also
receives DMRS transmissions in response to the requesting in order
to estimate a raw channel.
In another aspect of the present disclosure, an apparatus for
wireless communications at a user equipment (UE), includes a
processor and memory coupled with the processor. Instructions
stored in the memory are operable, when executed by the processor,
to cause the apparatus to indicate, to a base station, a training
state of a machine learning model for a given channel condition.
The apparatus also requests a change in demodulation reference
signal (DMRS) transmissions. The apparatus receives DMRS
transmissions in response to the request, based on the indicated
training state. The apparatus can perform online training of the
machine learning model with the received DMRS transmissions.
In another aspect of the present disclosure, an apparatus for
wireless communications at a user equipment (UE), includes a
processor and memory coupled with the processor. Instructions
stored in the memory are operable, when executed by the processor,
cause the apparatus to request, from a base station, a specific
number of demodulation reference signal (DMRS) symbols for a slot.
The apparatus can also receive DMRS transmissions in response to
the request, to estimate a raw channel.
In another aspect of the present disclosure, a UE includes means
for indicating, to a base station, a training state of a machine
learning model for a given channel condition. It also includes
requesting a change in demodulation reference signal (DMRS)
transmissions. The UE also includes means for receiving DMRS
transmissions in response to the request, based on the indicated
training state. The UE further includes means for performing online
training of the machine learning model with the DMRS
transmissions.
In another aspect of the present disclosure, a UE includes means
for requesting, from a base station, a specific number of
demodulation reference signal (DMRS) symbols for a slot. The UE
also includes means for receiving DMRS transmissions in accordance
with the request, to estimate a raw channel.
In another aspect of the present disclosure, a non-transitory
computer-readable medium with program code recorded thereon is
disclosed. The program code is executed by a user equipment (UE)
and includes program code to indicate, to a base station, a
training state of a machine learning model for a given channel
condition. It also includes a request for a change in demodulation
reference signal (DMRS) transmissions. The UE includes program code
to receive DMRS transmissions in response to the request, based on
the indicated training state. The UE further includes program code
to perform online training of the machine learning model with the
DMRS transmissions.
In another aspect of the present disclosure, a non-transitory
computer-readable medium with program code recorded thereon is
disclosed. The program code is executed by a user equipment (UE)
and includes program code to request, from a base station, a
specific number of demodulation reference signal (DMRS) symbols for
a slot. The UE also includes program code to receive DMRS
transmissions in accordance with the request, to estimate a raw
channel.
Aspects generally include a method, apparatus, system, computer
program product, non-transitory computer-readable medium, user
equipment, base station, wireless communication device, and
processing system as substantially described with reference to and
as illustrated by the accompanying drawings and specification.
The foregoing has outlined rather broadly the features and
technical advantages of examples according to the disclosure in
order that the detailed description that follows may be better
understood. Additional features and advantages will be described.
The conception and specific examples disclosed may be readily
utilized as a basis for modifying or designing other structures for
carrying out the same purposes of the present disclosure. Such
equivalent constructions do not depart from the scope of the
appended claims. Characteristics of the concepts disclosed, both
their organization and method of operation, together with
associated advantages will be better understood from the following
description when considered in connection with the accompanying
figures. Each of the figures is provided for the purposes of
illustration and description, and not as a definition of the limits
of the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
So that features of the present disclosure can be understood in
detail, a particular description, may be had by reference to
aspects, some of which are illustrated in the appended drawings. It
is to be noted, however, that the appended drawings illustrate only
certain aspects of this disclosure and are therefore not to be
considered limiting of its scope, for the description may admit to
other equally effective aspects. The same reference numbers in
different drawings may identify the same or similar elements.
FIG. 1 is a block diagram conceptually illustrating an example of a
wireless communications network, in accordance with various aspects
of the present disclosure.
FIG. 2 is a block diagram conceptually illustrating an example of a
base station in communication with a user equipment (UE) in a
wireless communications network, in accordance with various aspects
of the present disclosure.
FIG. 3 illustrates an example implementation of designing a neural
network using a system-on-a-chip (SOC), including a general-purpose
processor, in accordance with certain aspects of the present
disclosure.
FIGS. 4A, 4B, and 4C are diagrams illustrating a neural network, in
accordance with aspects of the present disclosure.
FIG. 4D is a diagram illustrating an exemplary deep convolutional
network (DCN), in accordance with aspects of the present
disclosure.
FIG. 5 is a block diagram illustrating an exemplary deep
convolutional network (DCN), in accordance with aspects of the
present disclosure.
FIG. 6 is a call flow diagram for channel adaptive demodulation
reference signal (DMRS) transmission based on UE feedback, in
accordance with various aspects of the present disclosure.
FIG. 7 is a block diagram illustrating transmit and receive beams,
according to aspects of the present disclosure.
FIG. 8 is a flow diagram illustrating an example process performed,
for example, by a user equipment, in accordance with various
aspects of the present disclosure.
FIG. 9 is a flow diagram illustrating an example process performed,
for example, by a user equipment, in accordance with various
aspects of the present disclosure.
DETAILED DESCRIPTION
Various aspects of the disclosure are described more fully below
with reference to the accompanying drawings. This disclosure may,
however, be embodied in many different forms and should not be
construed as limited to any specific structure or function
presented throughout this disclosure. Rather, these aspects are
provided so that this disclosure will be thorough and complete, and
will fully convey the scope of the disclosure to those skilled in
the art. Based on the teachings, one skilled in the art should
appreciate that the scope of the disclosure is intended to cover
any aspect of the disclosure, whether implemented independently of
or combined with any other aspect of the disclosure. For example,
an apparatus may be implemented or a method may be practiced using
any number of the aspects set forth. In addition, the scope of the
disclosure is intended to cover such an apparatus or method which
is practiced using other structure, functionality, or structure and
functionality in addition to or other than the various aspects of
the disclosure set forth. It should be understood that any aspect
of the disclosure disclosed may be embodied by one or more elements
of a claim.
Several aspects of telecommunications systems will now be presented
with reference to various apparatuses and techniques. These
apparatuses and techniques will be described in the following
detailed description and illustrated in the accompanying drawings
by various blocks, modules, components, circuits, steps, processes,
algorithms, and/or the like (collectively referred to as
"elements"). These elements may be implemented using hardware,
software, or combinations thereof. Whether such elements are
implemented as hardware or software depends upon the particular
application and design constraints imposed on the overall
system.
It should be noted that while aspects may be described using
terminology commonly associated with 5G and later wireless
technologies, aspects of the present disclosure can be applied in
other generation-based communications systems, such as and
including 3G and/or 4G technologies.
Machine learning may be beneficial for wireless communications. For
example, learning-based receivers can learn to estimate channel
conditions from training data. After training, the learning-based
receivers may estimate (e.g., infer) characteristics of channels.
The receiver can rely upon the inferred channel characteristics
instead of, or as a supplement, to conventional channel estimation.
These receivers are also referred to as data-driven receivers.
As wireless communications occur between base stations and UEs, a
pilot signal, such as demodulation reference signal (DMRS), may be
transmitted to facilitate demodulation of data. According to an
aspect of the present disclosure, a channel adaptive demodulation
reference signal (DMRS) transmission is based on UE feedback. The
channel adaptive DMRS may have applications with, for example,
data-driven receivers or with beamformed communications.
In one aspect of the present disclosure, a data-driven user
equipment (UE) receiver may be well-trained (e.g., offline) for
particular channel conditions. As described below, a measure of
decoding quality may determine whether the receiver is
well-trained. The UE may indicate the state of training (e.g., well
trained or not) to the base station (e.g., gNB). In response, the
base station may transmit the demodulation reference signal (DMRS)
less frequently for this UE, for the purpose of online training. In
another aspect of the present disclosure, if for a given channel
condition the UE has not been well-trained, the UE may request more
frequent DMRS transmissions from the base station.
According to aspects of the present disclosure, the UE may transmit
the feedback over a physical uplink control channel (PUCCH). In
another aspect, the feedback from the UE can explicitly mention the
reason for the request.
An adaptive, configurable DMRS has other applications. For example,
in a millimeter wave system (e.g., frequency range two (FR2)), beam
management procedures are executed to obtain best transmit beams
and receive beams from the base station and UE, respectively, for a
downlink scenario. Accordingly, downlink communications occur via
the beamformed channel, which includes the best transmit and
receive beams. The effective beamformed channel, however, only
represents a portion of the overall channel.
According to aspects of the present disclosure, to obtain an
estimate of the raw channel, the UE requests additional DMRSs. The
UE sweeps through the receive beams to receive each additional DMRS
on a different receive beam. In one aspect, the UE requests a
specific number of DMRS transmissions (e.g., four). Based on the
measurements from the multiple receive beams, the UE may select a
better receive beam, directed more specifically towards an incident
beam. Thus, the additional overhead of extra DMRSs can be
compensated for by improved throughput based on a better channel
estimate.
FIG. 1 is a diagram illustrating a network 100 in which aspects of
the present disclosure may be practiced. The network 100 may be a
5G or NR network or some other wireless network, such as an LTE
network. The wireless network 100 may include a number of BSs 110
(shown as BS 110a, BS 110b, BS 110c, and BS 110d) and other network
entities. A BS is an entity that communicates with user equipment
(UEs) and may also be referred to as a base station, a NR BS, a
Node B, a gNB, a 5G node B (NB), an access point, a transmit
receive point (TRP), and/or the like. Each BS may provide
communications coverage for a particular geographic area. In 3GPP,
the term "cell" can refer to a coverage area of a BS and/or a BS
subsystem serving this coverage area, depending on the context in
which the term is used.
A BS may provide communications coverage for a macro cell, a pico
cell, a femto cell, and/or another type of cell. A macro cell may
cover a relatively large geographic area (e.g., several kilometers
in radius) and may allow unrestricted access by UEs with service
subscription. A pico cell may cover a relatively small geographic
area and may allow unrestricted access by UEs with service
subscription. A femto cell may cover a relatively small geographic
area (e.g., a home) and may allow restricted access by UEs having
association with the femto cell (e.g., UEs in a closed subscriber
group (CSG)).
A BS for a macro cell may be referred to as a macro BS. A BS for a
pico cell may be referred to as a pico BS. A BS for a femto cell
may be referred to as a femto BS or a home BS. In the example shown
in FIG. 1, a BS 110a may be a macro BS for a macro cell 102a, a BS
110b may be a pico BS for a pico cell 102b, and a BS 110c may be a
femto BS for a femto cell 102c. A BS may support one or multiple
(e.g., three) cells. The terms "eNB", "base station", "NR BS",
"gNB", "TRP", "AP", "node B", "5G NB", and "cell" may be used
interchangeably.
In some aspects, a cell may not necessarily be stationary, and the
geographic area of the cell may move according to the location of a
mobile BS. In some aspects, the BSs may be interconnected to one
another and/or to one or more other BSs or network nodes (not
shown) in the wireless network 100 through various types of
backhaul interfaces such as a direct physical connection, a virtual
network, and/or the like using any suitable transport network.
The wireless network 100 may also include relay stations. A relay
station is an entity that can receive a transmission of data from
an upstream station (e.g., a BS or a UE) and send a transmission of
the data to a downstream station (e.g., a UE or a BS). A relay
station may also be a UE that can relay transmissions for other
UEs. In the example shown in FIG. 1, a relay station 110d may
communicate with macro BS 110a and a UE 120d in order to facilitate
communications between the BS 110a and UE 120d. A relay station may
also be referred to as a relay BS, a relay base station, a relay,
and/or the like.
The wireless network 100 may be a heterogeneous network that
includes BSs of different types, e.g., macro BSs, pico BSs, femto
BSs, relay BSs, and/or the like. These different types of BSs may
have different transmit power levels, different coverage areas, and
different impact on interference in the wireless network 100. For
example, macro BSs may have a high transmit power level (e.g., 5 to
40 Watts) whereas pico BSs, femto BSs, and relay BSs may have lower
transmit power levels (e.g., 0.1 to 2 Watts).
A network controller 130 may couple to a set of BSs and may provide
coordination and control for these BSs. The network controller 130
may communicate with the BSs via a backhaul. The BSs may also
communicate with one another, e.g., directly or indirectly via a
wireless or wireline backhaul.
UEs 120 (e.g., 120a, 120b, 120c) may be dispersed throughout the
wireless network 100, and each UE may be stationary or mobile. A UE
may also be referred to as an access terminal, a terminal, a mobile
station, a subscriber unit, a station, and/or the like. A UE may be
a cellular phone (e.g., a smart phone), a personal digital
assistant (PDA), a wireless modem, a wireless communications
device, a handheld device, a laptop computer, a cordless phone, a
wireless local loop (WLL) station, a tablet, a camera, a gaming
device, a netbook, a smartbook, an ultrabook, a medical device or
equipment, biometric sensors/devices, wearable devices (smart
watches, smart clothing, smart glasses, smart wrist bands, smart
jewelry (e.g., smart ring, smart bracelet), an entertainment device
(e.g., a music or video device, or a satellite radio), a vehicular
component or sensor, smart meters/sensors, industrial manufacturing
equipment, a global positioning system device, or any other
suitable device that is configured to communicate via a wireless or
wired medium.
Some UEs may be considered machine-type communications (MTC) or
evolved or enhanced machine-type communications (eMTC) UEs. MTC and
eMTC UEs include, for example, robots, drones, remote devices,
sensors, meters, monitors, location tags, and/or the like, that may
communicate with a base station, another device (e.g., remote
device), or some other entity. A wireless node may provide, for
example, connectivity for or to a network (e.g., a wide area
network such as Internet or a cellular network) via a wired or
wireless communications link. Some UEs may be considered
Internet-of-Things (IoT) devices, and/or may be implemented as
NB-IoT (narrowband internet of things) devices. Some UEs may be
considered a customer premises equipment (CPE). UE 120 may be
included inside a housing that houses components of UE 120, such as
processor components, memory components, and/or the like.
In general, any number of wireless networks may be deployed in a
given geographic area. Each wireless network may support a
particular RAT and may operate on one or more frequencies. A RAT
may also be referred to as a radio technology, an air interface,
and/or the like. A frequency may also be referred to as a carrier,
a frequency channel, and/or the like. Each frequency may support a
single RAT in a given geographic area in order to avoid
interference between wireless networks of different RATs. In some
cases, NR or 5G RAT networks may be deployed.
In some aspects, two or more UEs 120 (e.g., shown as UE 120a and UE
120e) may communicate directly using one or more sidelink channels
(e.g., without using a base station 110 as an intermediary to
communicate with one another). For example, the UEs 120 may
communicate using peer-to-peer (P2P) communications,
device-to-device (D2D) communications, a vehicle-to-everything
(V2X) protocol (e.g., which may include a vehicle-to-vehicle (V2V)
protocol, a vehicle-to-infrastructure (V2I) protocol, and/or the
like), a mesh network, and/or the like. In this case, the UE 120
may perform scheduling operations, resource selection operations,
and/or other operations described elsewhere as being performed by
the base station 110. For example, the base station 110 may
configure a UE 120 via downlink control information (DCI), radio
resource control (RRC) signaling, a media access control-control
element (MAC-CE) or via system information (e.g., a system
information block (SIB).
As indicated above, FIG. 1 is provided merely as an example. Other
examples may differ from what is described with regard to FIG.
1.
FIG. 2 shows a block diagram of a design 200 of the base station
110 and UE 120, which may be one of the base stations and one of
the UEs in FIG. 1. The base station 110 may be equipped with T
antennas 234a through 234t, and UE 120 may be equipped with R
antennas 252a through 252r, where in general T.gtoreq.1 and
R.gtoreq.1.
At the base station 110, a transmit processor 220 may receive data
from a data source 212 for one or more UEs, select one or more
modulation and coding schemes (MCS) for each UE based at least in
part on channel quality indicators (CQIs) received from the UE,
process (e.g., encode and modulate) the data for each UE based at
least in part on the MCS(s) selected for the UE, and provide data
symbols for all UEs. Decreasing the MCS lowers throughput but
increases reliability of the transmission. The transmit processor
220 may also process system information (e.g., for semi-static
resource partitioning information (SRPI) and/or the like) and
control information (e.g., CQI requests, grants, upper layer
signaling, and/or the like) and provide overhead symbols and
control symbols. The transmit processor 220 may also generate
reference symbols for reference signals (e.g., the cell-specific
reference signal (CRS)) and synchronization signals (e.g., the
primary synchronization signal (PSS) and secondary synchronization
signal (SSS)). A transmit (TX) multiple-input multiple-output
(MIMO) processor 230 may perform spatial processing (e.g.,
precoding) on the data symbols, the control symbols, the overhead
symbols, and/or the reference symbols, if applicable, and may
provide T output symbol streams to T modulators (MODs) 232a through
232t. Each modulator 232 may process a respective output symbol
stream (e.g., for OFDM and/or the like) to obtain an output sample
stream. Each modulator 232 may further process (e.g., convert to
analog, amplify, filter, and upconvert) the output sample stream to
obtain a downlink signal. T downlink signals from modulators 232a
through 232t may be transmitted via T antennas 234a through 234t,
respectively. According to various aspects described in more detail
below, the synchronization signals can be generated with location
encoding to convey additional information.
At the UE 120, antennas 252a through 252r may receive the downlink
signals from the base station 110 and/or other base stations and
may provide received signals to demodulators (DEMODs) 254a through
254r, respectively. Each demodulator 254 may condition (e.g.,
filter, amplify, downconvert, and digitize) a received signal to
obtain input samples. Each demodulator 254 may further process the
input samples (e.g., for OFDM and/or the like) to obtain received
symbols. A MIMO detector 256 may obtain received symbols from all R
demodulators 254a through 254r, perform MIMO detection on the
received symbols if applicable, and provide detected symbols. A
receive processor 258 may process (e.g., demodulate and decode) the
detected symbols, provide decoded data for the UE 120 to a data
sink 260, and provide decoded control information and system
information to a controller/processor 280. A channel processor may
determine reference signal received power (RSRP), received signal
strength indicator (RSSI), reference signal received quality
(RSRQ), channel quality indicator (CQI), and/or the like. In some
aspects, one or more components of the UE 120 may be included in a
housing.
On the uplink, at the UE 120, a transmit processor 264 may receive
and process data from a data source 262 and control information
(e.g., for reports comprising RSRP, RSSI, RSRQ, CQI, and/or the
like) from the controller/processor 280. Transmit processor 264 may
also generate reference symbols for one or more reference signals.
The symbols from the transmit processor 264 may be precoded by a TX
MIMO processor 266 if applicable, further processed by modulators
254a through 254r (e.g., for DFT-s-OFDM, CP-OFDM, and/or the like),
and transmitted to the base station 110. At the base station 110,
the uplink signals from the UE 120 and other UEs may be received by
the antennas 234, processed by the demodulators 254, detected by a
MIMO detector 236 if applicable, and further processed by a receive
processor 238 to obtain decoded data and control information sent
by the UE 120. The receive processor 238 may provide the decoded
data to a data sink 239 and the decoded control information to a
controller/processor 240. The base station 110 may include
communications unit 244 and communicate to the network controller
130 via the communications unit 244. The network controller 130 may
include a communications unit 294, a controller/processor 290, and
a memory 292.
The controller/processor 280 of the UE 120, and/or any other
component(s) of FIG. 2 may perform one or more techniques
associated with adaptive DMRS for machine learning as described in
more detail elsewhere. For example, the controller/processor 280 of
the UE 120, and/or any other component(s) of FIG. 2 may perform or
direct operations of, for example, the processes of FIGS. 8-9
and/or other processes as described. Memories 242 and 282 may store
data and program codes for the base station 110 and UE 120,
respectively. A scheduler 246 may schedule UEs for data
transmission on the downlink and/or uplink.
In some aspects, the UE 120 may include means for indicating, means
for receiving, means for performing, and/or means for requesting.
Such means may include one or more components of the UE 120
described in connection with FIG. 2.
As indicated above, FIG. 2 is provided merely as an example. Other
examples may differ from what is described with regard to FIG.
2.
In some cases, different types of devices supporting different
types of applications and/or services may coexist in a cell.
Examples of different types of devices include UE handsets,
customer premises equipment (CPEs), vehicles, Internet of Things
(IoT) devices, and/or the like. Examples of different types of
applications include ultra-reliable low-latency communications
(URLLC) applications, massive machine-type communications (mMTC)
applications, enhanced mobile broadband (eMBB) applications,
vehicle-to-anything (V2X) applications, and/or the like.
Furthermore, in some cases, a single device may support different
applications or services simultaneously.
FIG. 3 illustrates an example implementation of a system-on-a-chip
(SOC) 300, which may include a central processing unit (CPU) 302 or
a multi-core CPU configured for generating gradients for neural
network training, in accordance with certain aspects of the present
disclosure. The SOC 300 may be included in the base station 110 or
UE 120. Variables (e.g., neural signals and synaptic weights),
system parameters associated with a computational device (e.g.,
neural network with weights), delays, frequency bin information,
and task information may be stored in a memory block associated
with a neural processing unit (NPU) 308, in a memory block
associated with a CPU 302, in a memory block associated with a
graphics processing unit (GPU) 304, in a memory block associated
with a digital signal processor (DSP) 306, in a memory block 318,
or may be distributed across multiple blocks. Instructions executed
at the CPU 302 may be loaded from a program memory associated with
the CPU 302 or may be loaded from a memory block 318.
The SOC 300 may also include additional processing blocks tailored
to specific functions, such as a GPU 304, a DSP 306, a connectivity
block 310, which may include fifth generation (5G) connectivity,
fourth generation long term evolution (4G LTE) connectivity, Wi-Fi
connectivity, USB connectivity, Bluetooth connectivity, and the
like, and a multimedia processor 312 that may, for example, detect
and recognize gestures. In one implementation, the NPU is
implemented in the CPU, DSP, and/or GPU. The SOC 300 may also
include a sensor processor 314, image signal processors (ISPs) 316,
and/or navigation module 320, which may include a global
positioning system.
The SOC 300 may be based on an ARM instruction set. In an aspect of
the present disclosure, the instructions loaded into the
general-purpose processor 302 may comprise code to indicate, to a
base station, a training state of a machine learning model for a
given channel condition, and a request for a change in demodulation
reference signal (DMRS) transmissions. Additionally, the
general-purpose processor 302 may comprise code to receive DMRS
transmissions in accordance with the training state for the given
channel condition, and code to perform online training of the
machine learning model with the DMRS transmission. The
general-purpose processor 302 may further comprise code to request,
from a base station, a specific number of DMRS symbols for a slot,
and code to receive DMRS transmissions in response to the request,
to estimate a raw channel.
Deep learning architectures may perform an object recognition task
by learning to represent inputs at successively higher levels of
abstraction in each layer, thereby building up a useful feature
representation of the input data. In this way, deep learning
addresses a major bottleneck of traditional machine learning. Prior
to the advent of deep learning, a machine learning approach to an
object recognition problem may have relied heavily on human
engineered features, perhaps in combination with a shallow
classifier. A shallow classifier may be a two-class linear
classifier, for example, in which a weighted sum of the feature
vector components may be compared with a threshold to predict to
which class the input belongs. Human engineered features may be
templates or kernels tailored to a specific problem domain by
engineers with domain expertise. Deep learning architectures, in
contrast, may learn to represent features that are similar to what
a human engineer might design, but through training. Furthermore, a
deep network may learn to represent and recognize new types of
features that a human might not have considered.
A deep learning architecture may learn a hierarchy of features. If
presented with visual data, for example, the first layer may learn
to recognize relatively simple features, such as edges, in the
input stream. In another example, if presented with auditory data,
the first layer may learn to recognize spectral power in specific
frequencies. The second layer, taking the output of the first layer
as input, may learn to recognize combinations of features, such as
simple shapes for visual data or combinations of sounds for
auditory data. For instance, higher layers may learn to represent
complex shapes in visual data or words in auditory data. Still
higher layers may learn to recognize common visual objects or
spoken phrases.
Deep learning architectures may perform especially well when
applied to problems that have a natural hierarchical structure. For
example, the classification of motorized vehicles may benefit from
first learning to recognize wheels, windshields, and other
features. These features may be combined at higher layers in
different ways to recognize cars, trucks, and airplanes.
Neural networks may be designed with a variety of connectivity
patterns. In feed-forward networks, information is passed from
lower to higher layers, with each neuron in a given layer
communicating to neurons in higher layers. A hierarchical
representation may be built up in successive layers of a
feed-forward network, as described above. Neural networks may also
have recurrent or feedback (also called top-down) connections. In a
recurrent connection, the output from a neuron in a given layer may
be communicated to another neuron in the same layer. A recurrent
architecture may be helpful in recognizing patterns that span more
than one of the input data chunks that are delivered to the neural
network in a sequence. A connection from a neuron in a given layer
to a neuron in a lower layer is called a feedback (or top-down)
connection. A network with many feedback connections may be helpful
when the recognition of a high-level concept may aid in
discriminating the particular low-level features of an input.
The connections between layers of a neural network may be fully
connected or locally connected. FIG. 4A illustrates an example of a
fully connected neural network 402. In a fully connected neural
network 402, a neuron in a first layer may communicate its output
to every neuron in a second layer, so that each neuron in the
second layer will receive input from every neuron in the first
layer. FIG. 4B illustrates an example of a locally connected neural
network 404. In a locally connected neural network 404, a neuron in
a first layer may be connected to a limited number of neurons in
the second layer. More generally, a locally connected layer of the
locally connected neural network 404 may be configured so that each
neuron in a layer will have the same or a similar connectivity
pattern, but with connections strengths that may have different
values (e.g., 410, 412, 414, and 416). The locally connected
connectivity pattern may give rise to spatially distinct receptive
fields in a higher layer, because the higher layer neurons in a
given region may receive inputs that are tuned through training to
the properties of a restricted portion of the total input to the
network.
One example of a locally connected neural network is a
convolutional neural network. FIG. 4C illustrates an example of a
convolutional neural network 406. The convolutional neural network
406 may be configured such that the connection strengths associated
with the inputs for each neuron in the second layer are shared
(e.g., 408). Convolutional neural networks may be well suited to
problems in which the spatial location of inputs is meaningful.
One type of convolutional neural network is a deep convolutional
network (DCN). FIG. 4D illustrates a detailed example of a DCN 400
designed to recognize visual features from an image 426 input from
an image capturing device 430, such as a car-mounted camera. The
DCN 400 of the current example may be trained to identify traffic
signs and a number provided on the traffic sign. Of course, the DCN
400 may be trained for other tasks, such as identifying lane
markings or identifying traffic lights.
The DCN 400 may be trained with supervised learning. During
training, the DCN 400 may be presented with an image, such as the
image 426 of a speed limit sign, and a forward pass may then be
computed to produce an output 422. The DCN 400 may include a
feature extraction section and a classification section. Upon
receiving the image 426, a convolutional layer 432 may apply
convolutional kernels (not shown) to the image 426 to generate a
first set of feature maps 418. As an example, the convolutional
kernel for the convolutional layer 432 may be a 5.times.5 kernel
that generates 28.times.28 feature maps. In the present example,
because four different feature maps are generated in the first set
of feature maps 418, four different convolutional kernels were
applied to the image 426 at the convolutional layer 432. The
convolutional kernels may also be referred to as filters or
convolutional filters.
The first set of feature maps 418 may be subsampled by a max
pooling layer (not shown) to generate a second set of feature maps
420. The max pooling layer reduces the size of the first set of
feature maps 418. That is, a size of the second set of feature maps
420, such as 14.times.14, is less than the size of the first set of
feature maps 418, such as 28.times.28. The reduced size provides
similar information to a subsequent layer while reducing memory
consumption. The second set of feature maps 420 may be further
convolved via one or more subsequent convolutional layers (not
shown) to generate one or more subsequent sets of feature maps (not
shown).
In the example of FIG. 4D, the second set of feature maps 420 is
convolved to generate a first feature vector 424. Furthermore, the
first feature vector 424 is further convolved to generate a second
feature vector 428. Each feature of the second feature vector 428
may include a number that corresponds to a possible feature of the
image 426, such as "sign," "60," and "100." A softmax function (not
shown) may convert the numbers in the second feature vector 428 to
a probability. As such, an output 422 of the DCN 400 is a
probability of the image 426 including one or more features.
In the present example, the probabilities in the output 422 for
"sign" and "60" are higher than the probabilities of the others of
the output 422, such as "30," "40," "50," "70," "80," "90," and
"100". Before training, the output 422 produced by the DCN 400 is
likely to be incorrect. Thus, an error may be calculated between
the output 422 and a target output. The target output is the ground
truth of the image 426 (e.g., "sign" and "60"). The weights of the
DCN 400 may then be adjusted so the output 422 of the DCN 400 is
more closely aligned with the target output.
To adjust the weights, a learning algorithm may compute a gradient
vector for the weights. The gradient may indicate an amount that an
error would increase or decrease if the weight were adjusted. At
the top layer, the gradient may correspond directly to the value of
a weight connecting an activated neuron in the penultimate layer
and a neuron in the output layer. In lower layers, the gradient may
depend on the value of the weights and on the computed error
gradients of the higher layers. The weights may then be adjusted to
reduce the error. This manner of adjusting the weights may be
referred to as "back propagation" as it involves a "backward pass"
through the neural network.
In practice, the error gradient of weights may be calculated over a
small number of examples, so that the calculated gradient
approximates the true error gradient. This approximation method may
be referred to as stochastic gradient descent. Stochastic gradient
descent may be repeated until the achievable error rate of the
entire system has stopped decreasing or until the error rate has
reached a target level. After learning, the DCN may be presented
with new images (e.g., the speed limit sign of the image 426) and a
forward pass through the network may yield an output 422 that may
be considered an inference or a prediction of the DCN.
Deep belief networks (DBNs) are probabilistic models comprising
multiple layers of hidden nodes. DBNs may be used to extract a
hierarchical representation of training data sets. A DBN may be
obtained by stacking up layers of Restricted Boltzmann Machines
(RBMs). An RBM is a type of artificial neural network that can
learn a probability distribution over a set of inputs. Because RBMs
can learn a probability distribution in the absence of information
about the class to which each input should be categorized, RBMs are
often used in unsupervised learning. Using a hybrid unsupervised
and supervised paradigm, the bottom RBMs of a DBN may be trained in
an unsupervised manner and may serve as feature extractors, and the
top RBM may be trained in a supervised manner (on a joint
distribution of inputs from the previous layer and target classes)
and may serve as a classifier.
Deep convolutional networks (DCNs) are networks of convolutional
networks, configured with additional pooling and normalization
layers. DCNs have achieved state-of-the-art performance on many
tasks. DCNs can be trained using supervised learning in which both
the input and output targets are known for many exemplars and are
used to modify the weights of the network by use of gradient
descent methods.
DCNs may be feed-forward networks. In addition, as described above,
the connections from a neuron in a first layer of a DCN to a group
of neurons in the next higher layer are shared across the neurons
in the first layer. The feed-forward and shared connections of DCNs
may be exploited for fast processing. The computational burden of a
DCN may be much less, for example, than that of a similarly sized
neural network that comprises recurrent or feedback
connections.
The processing of each layer of a convolutional network may be
considered a spatially invariant template or basis projection. If
the input is first decomposed into multiple channels, such as the
red, green, and blue channels of a color image, then the
convolutional network trained on that input may be considered
three-dimensional, with two spatial dimensions along the axes of
the image and a third dimension capturing color information. The
outputs of the convolutional connections may be considered to form
a feature map in the subsequent layer, with each element of the
feature map (e.g., 220) receiving input from a range of neurons in
the previous layer (e.g., feature maps 218) and from each of the
multiple channels. The values in the feature map may be further
processed with a non-linearity, such as a rectification, max(0, x).
Values from adjacent neurons may be further pooled, which
corresponds to down sampling, and may provide additional local
invariance and dimensionality reduction. Normalization, which
corresponds to whitening, may also be applied through lateral
inhibition between neurons in the feature map.
The performance of deep learning architectures may increase as more
labeled data points become available or as computational power
increases. Modern deep neural networks are routinely trained with
computing resources that are thousands of times greater than what
was available to a typical researcher just fifteen years ago. New
architectures and training paradigms may further boost the
performance of deep learning. Rectified linear units may reduce a
training issue known as vanishing gradients. New training
techniques may reduce over-fitting and thus enable larger models to
achieve better generalization. Encapsulation techniques may
abstract data in a given receptive field and further boost overall
performance.
FIG. 5 is a block diagram illustrating a deep convolutional network
550. The deep convolutional network 550 may include multiple
different types of layers based on connectivity and weight sharing.
As shown in FIG. 5, the deep convolutional network 550 includes the
convolution blocks 554A, 554B. Each of the convolution blocks 554A,
554B may be configured with a convolution layer (CONV) 356, a
normalization layer (LNorm) 558, and a max pooling layer (MAX POOL)
560.
The convolution layers 556 may include one or more convolutional
filters, which may be applied to the input data to generate a
feature map. Although only two of the convolution blocks 554A, 554B
are shown, the present disclosure is not so limiting, and instead,
any number of the convolution blocks 554A, 554B may be included in
the deep convolutional network 550 according to design preference.
The normalization layer 558 may normalize the output of the
convolution filters. For example, the normalization layer 558 may
provide whitening or lateral inhibition. The max pooling layer 560
may provide down sampling aggregation over space for local
invariance and dimensionality reduction.
The parallel filter banks, for example, of a deep convolutional
network may be loaded on a CPU 302 or GPU 304 of an SOC 300 to
achieve high performance and low power consumption. In alternative
embodiments, the parallel filter banks may be loaded on the DSP 306
or an ISP 316 of an SOC 300. In addition, the deep convolutional
network 550 may access other processing blocks that may be present
on the SOC 300, such as sensor processor 314 and navigation module
320, dedicated, respectively, to sensors and navigation.
The deep convolutional network 550 may also include one or more
fully connected layers 562 (FC1 and FC2). The deep convolutional
network 550 may further include a logistic regression (LR) layer
564. Between each layer 556, 558, 560, 562, 564 of the deep
convolutional network 550 are weights (not shown) that are to be
updated. The output of each of the layers (e.g., 556, 558, 560,
562, 564) may serve as an input of a succeeding one of the layers
(e.g., 556, 558, 560, 562, 564) in the deep convolutional network
550 to learn hierarchical feature representations from input data
552 (e.g., images, audio, video, sensor data and/or other input
data) supplied at the first of the convolution blocks 554A. The
output of the deep convolutional network 550 is a classification
score 566 for the input data 552. The classification score 566 may
be a set of probabilities, where each probability is the
probability of the input data, including a feature from a set of
features.
As indicated above, FIGS. 3-5 are provided as examples. Other
examples may differ from what is described with respect to FIGS.
3-5.
Machine learning may be beneficial for wireless communications. For
example, learning-based receivers can learn to estimate (e.g.,
infer) channel conditions from training data. After training, the
learning-based receivers may infer characteristics of channels. The
receiver can rely upon the inferred channel characteristics instead
of, or as a supplement to, conventional channel estimation. These
receivers are also referred to as data-driven receivers.
A learning-based receiver architecture may be considered, for
example, to avoid channel estimation in more challenging scenarios,
such as in low-resolution analog-to-digital converter (ADC)
systems. A class of these receivers decodes quantized signals
independent of explicit channel estimation. The described class of
receivers may learn characteristics of the quantized outputs at
each beam, instead of estimating the channel state information
(CSI), by considering the channel and quantization functions as a
black box. Data-driven receivers may have less dependence on
channel estimation, particularly if they have been well-trained for
certain channel conditions.
The described receivers may be trained to learn channel conditions
based on received reference signals (RSs). The reference signal
training may be online training. As a result of the learning, the
receivers may omit explicit channel estimation, and for instance,
may instead look at a sequence of the transmitted and received
training symbols to infer the channel.
As wireless communications occur between base stations and UEs, a
pilot signal, such as demodulation reference signal (DMRS), may be
transmitted to facilitate demodulation of data. The DMRS is
utilized by a wireless communications device to produce channel
estimates for demodulation of an associated physical channel. The
DMRS may be device-specific, and thus, directly corresponds to data
targeted to that particular UE. The DMRS may be transmitted on
demand. The receivers may train on DMRSs.
According to an aspect of the present disclosure, a channel
adaptive demodulation reference signal (DMRS) transmission is based
on UE feedback. The channel adaptive DMRS may have applications
with, for example, data-driven receivers with beamformed
channels.
In one aspect of the present disclosure, a data-driven user
equipment (UE) receiver may be well-trained (e.g., offline) for
particular channel conditions. The UE may indicate this state of
training to the base station (e.g., gNB). In response, the base
station may transmit the demodulation reference signal (DMRS) less
frequently for this UE, for the purpose of online training. For
example, the base station may skip the DMRS for a slot or a few
slots altogether. In another aspect, the base station may use a
light training DMRS pattern (e.g., transmitting on fewer symbols),
etc., for online training.
In another aspect of the present disclosure, if for a given channel
condition the UE has not been well-trained, the UE may request more
frequent DMRS transmissions from the base station. Alternatively,
the UE may request a heavier DMRS pattern, etc., to perform online
training and improve decoding quality. As an example, a number of
symbols for the DMRS may increase for the heavier DMRS pattern.
According to an aspect of the present disclosure, a criterion for
the UE to determine whether the machine learning model is
well-trained is a measure of decoding quality. In one example, a
measure of decoding quality is a bit error rate (BER).
In this example, if the bit error rate is less than a threshold,
the UE may request less frequent DMRS training (and/or a light DMRS
pattern), implying a performance of the previously trained receiver
is equal to or greater than a performance threshold. If the bit
error rate is larger than the threshold, the UE may request more
frequent DMRS training (and/or a heavy DMRS pattern), implying the
performance of the previously trained receiver is less than the
performance threshold, and needs to improve through online
training.
In summary, for some channel realizations, the UE may request more
frequent reference signal transmission (e.g., because the UE has
not been trained for similar channel realizations in the offline
training phase). For other channel realizations, the UE may have
been well-trained, and therefore the UE can request less frequent
reference signal transmission.
According to aspects of the present disclosure, the UE may transmit
the feedback over a physical uplink control channel (PUCCH). In
another aspect, the feedback from the UE can explicitly mention the
reason for the request. The reason may be for online training
purposes, etc.
In still another aspect of the present disclosure, the DMRS used
for online training of the data-driven receiver may be defined
particularly for this purpose. That is, the DMRS pattern for online
training may be different from legacy DMRS patterns defined in the
current 3GPP specifications.
FIG. 6 is a call flow diagram for channel adaptive demodulation
reference signal (DMRS) transmission based on UE feedback, in
accordance with various aspects of the present disclosure. At time
t1, a UE 602 determines an offline training state for particular
channel conditions. For example, a machine learning model may be
well-trained or poorly-trained for the particular channel
conditions. As described above, comparison of a decoding quality to
a performance threshold may determine whether the machine learning
model is well-trained or poorly-trained. At time t2, the UE informs
a base station 604 of the training state. In other aspects, the UE
may explicitly request a given number of DMRS symbols with a given
rank to facilitate raw channel estimation (as described in more
detail below). In response, at time t3, the base station configures
a DMRS based on the training state. For example, the base station
604 may select a more frequent DMRS or a heavier DMRS pattern if
the machine learning model is poorly-trained, in order to improve
training. At time t4, the base station 604 transmits the configured
DMRS. Finally, at time t5, the UE 602 trains its machine learning
model for the particular channel conditions based on the received
DMRS. By adaptively configuring the DMRS, the UE is able to more
efficiently train its machine learning models.
An adaptive, configurable DMRS has other applications, as well. For
example, in a millimeter wave system (e.g., frequency range two
(FR2)), beam management procedures are executed to obtain best
transmit beams and receive beams from the base station and UE,
respectively, for a downlink scenario. Accordingly, downlink
communications occur via the beamformed channel including the best
transmit and receive beams. The effective beamformed channel,
however, only represents a portion of the overall channel.
FIG. 7 is a block diagram illustrating transmit and receive beams,
according to aspects of the present disclosure. A UE 710
communicates across the downlink with a base station 712 via a
number of receive beams A1, A2, A3, and A4, as well as transmit
beams B1, B2, B3, B4, B5, and B6. In this example, the best receive
beam is the fourth beam A4 and the best transmit beam is the fifth
transmit beam B5. The observed channel is thus the beam formed
channel from the fifth transmit beam B5 and the fourth receive beam
A4. If an estimate of the overall, raw channel is desired, beam
sweeping may occur at the receiver side. For example, sweeping
across four beams (e.g., A4, A3, A2, and A1) enables the UE to
observe a 4.times.1 channel, instead of a 1.times.1 channel seen
with a single receive beam (e.g., the fourth receive beam A4).
According to aspects of the present disclosure, to obtain an
estimate of the raw channel, the UE 710 requests additional DMRSs.
The additional DMRSs may be repeated DMRSs or newly defined DMRSs.
In this aspect, the base station 712 transmits the DMRS four times
from the fifth transmit beam B5. The UE 710 sweeps through the
receive beams to receive each DMRS on a different receive beam
(e.g., A1, A2, A3, or A4). Thus, the UE can measure the channel
with a first beam A1 based on the first DMRS at a first symbol, can
measure the channel with a second beam A2 based on the second DMRS
at a second symbol, can measure the channel with a third beam A3
based on the third DMRS at a third symbol, and can measure the
channel with a fourth beam A4 based on the fourth DMRS at a fourth
symbol.
To enable these measurements of the raw channel, the UE adaptively
requests multiple DMRS transmissions. In other words, the UE
customizes the DMRS transmissions for a particular purpose, in this
case, raw channel estimation. In one aspect, the UE requests a
specific number of DMRS transmissions (e.g., four). In other
aspects, the UE also requests a specific rank for the given number
of symbols (e.g., a number of transmission layers). The DMRS
transmission may occur in consecutive symbols, for example, three
or four consecutive symbols.
The UE may request, from the base station, a given number of DMRS
symbols with a given rank. For a mmWave use case, rank one and rank
two transmissions are considered. In this case, the UE may request
four DMRS symbols, each with rank one, for example. Or the UE may
request four DMRS symbols, each with rank two, etc.
In some aspects, based on the measurements from the multiple
receive beams, the UE may select a better beam, directed more
specifically towards an incident beam. Thus, the additional
overhead of extra DMRSs can be compensated for by improved
throughput based on a better channel estimate. In contrast to
current specifications that employ DMRS for beamformed channel
estimation, aspects of the present disclosure apply to raw,
non-beamformed channel estimation through consecutive beamformed
measurements.
FIG. 8 is a diagram illustrating an example process 800 performed,
for example, by a UE, in accordance with various aspects of the
present disclosure. The example process 800 is an example of
adaptive demodulation reference signal (DMRS) transmission, for
example, with machine learning-based receivers.
As shown in FIG. 8, in some aspects, the process 800 may include
indicating, to a base station, a training state of a machine
learning model for a given channel condition, and a request for a
change in demodulation reference signal (DMRS) transmissions (block
802). For example, the user equipment (UE) (e.g., using the antenna
252, DEMOD/MOD 254, MIMO detector 256, TX MIMO processor 266,
receive processor 258, transmit processor 264, controller/processor
280, and/or memory 282) can indicate, to a base station, the
training state and the request. In some aspects, the process 800
may include receiving DMRS transmissions in accordance with the
training state for the given channel condition (block 804). For
example, the UE (e.g., using the antenna 252, DEMOD/MOD 254, MIMO
detector 256, receive processor 258, controller/processor 280,
and/or memory 282) can receive DMRS transmissions. The process 800
may also include performing online training of the machine learning
model with the DMRS transmission (block 806). For example, the UE
(e.g., using the antenna 252, DEMOD/MOD 254, MIMO detector 256, TX
MIMO processor 266, receive processor 258, transmit processor 264,
controller/processor 280, and/or memory 282) can perform online
training.
FIG. 9 is a diagram illustrating an example process 900 performed,
for example, by a UE, in accordance with various aspects of the
present disclosure. The example process 900 is an example of
adaptive demodulation reference signal (DMRS) transmission, for
example, with machine learning-based receivers.
As shown in FIG. 9, in some aspects, the process 900 may include
requesting, from a base station, a specific number of demodulation
reference signal (DMRS) symbols for a slot (block 902). For
example, the UE (e.g., using the antenna 252, DEMOD/MOD 254, TX
MIMO processor 266, transmit processor 264, controller/processor
280, and/or memory 282) can request the specific number of DMRS
symbols. In some aspects, the process 900 may include receiving,
DMRS transmissions in accordance with the requesting to estimate a
raw channel (block 904). For example, the UE (e.g., using the
antenna 252, DEMOD/MOD 254, MIMO detector 256, receive processor
258, controller/processor 280, and/or memory 282) can receive, the
DMRS transmissions.
The foregoing disclosure provides illustration and description, but
is not intended to be exhaustive or to limit the aspects to the
precise form disclosed. Modifications and variations may be made in
light of the above disclosure or may be acquired from practice of
the aspects.
As used, the term "component" is intended to be broadly construed
as hardware, firmware, and/or a combination of hardware and
software. As used, a processor is implemented in hardware,
firmware, and/or a combination of hardware and software.
Some aspects are described in connection with thresholds. As used,
satisfying a threshold may, depending on the context, refer to a
value being greater than the threshold, greater than or equal to
the threshold, less than the threshold, less than or equal to the
threshold, equal to the threshold, not equal to the threshold,
and/or the like.
It will be apparent that systems and/or methods described may be
implemented in different forms of hardware, firmware, and/or a
combination of hardware and software. The actual specialized
control hardware or software code used to implement these systems
and/or methods is not limiting of the aspects. Thus, the operation
and behavior of the systems and/or methods were described without
reference to specific software code--it being understood that
software and hardware can be designed to implement the systems
and/or methods based, at least in part, on the description.
Even though particular combinations of features are recited in the
claims and/or disclosed in the specification, these combinations
are not intended to limit the disclosure of various aspects. In
fact, many of these features may be combined in ways not
specifically recited in the claims and/or disclosed in the
specification. Although each dependent claim listed below may
directly depend on only one claim, the disclosure of various
aspects includes each dependent claim in combination with every
other claim in the claim set. A phrase referring to "at least one
of" a list of items refers to any combination of those items,
including single members. As an example, "at least one of: a, b, or
c" is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well
as any combination with multiples of the same element (e.g., a-a,
a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, and
c-c-c or any other ordering of a, b, and c).
No element, act, or instruction used should be construed as
critical or essential unless explicitly described as such. Also, as
used, the articles "a" and "an" are intended to include one or more
items, and may be used interchangeably with "one or more."
Furthermore, as used, the terms "set" and "group" are intended to
include one or more items (e.g., related items, unrelated items, a
combination of related and unrelated items, and/or the like), and
may be used interchangeably with "one or more." Where only one item
is intended, the phrase "only one" or similar language is used.
Also, as used, the terms "has," "have," "having," and/or the like
are intended to be open-ended terms. Further, the phrase "based on"
is intended to mean "based, at least in part, on" unless explicitly
stated otherwise.
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